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使用i-AlexNet架构对心脏监测的ECG信号进行AI驱动的实时分类

Manjur Kolhar¹, Raisa Nazir Ahmed Kazi², Hitesh Mohapatra³

¹Department Health Informatics, College of Applied Medical Sciences, King Faisal University, Al Hofuf 61421, Saudi Arabia.

Diagnostics (Basel, Switzerland)

|July 13, 2024

概括

人工智能 (AI) 通过分析患者数据来提高医疗保健诊断,以更快,更准确地检测疾病. 这项研究利用人工智能与心电图信号,在识别异常心律时达到98.8%的准确性.

科学领域:

生物医学工程生物医学工程
医疗保健中的人工智能
计算医学是一种计算医学.

背景情况:

医疗保健行业越来越多地利用人工智能 (AI) 来进行先进的数据分析.
人工智能为医疗环境中改善诊断,个性化治疗和运营效率提供了潜力.
分析心电图 (ECG) 信号对于诊断各种心脏疾病至关重要.

研究的目的:

开发和评估基于人工智能的方法,用于准确分类正常和异常的心电图信号.
通过先进的机器学习技术,提高心脏异常的诊断能力.
在临床实践中提高心电图解释的准确性和效率.

主要方法:

电脑心电图信号经过预处理以减少噪音和心跳细分.
执行多功能提取,然后进行功能选择的优化技术.
一个改进的AlexNet模型,称为i-AlexNet,用于对ECG信号进行分类.
拟议的方法使用PTB和MIT-BIH公共ECG数据库进行验证.

主要成果:

开发的AI方法在分类ECG信号方面表现出高性能.
在经过测试的数据库中,i-AlexNet分类器实现了98.8%的卓越准确性.
拟议的方法优于ECG分析文献中的现有技术.

结论:

这种由人工智能驱动的方法为检测异常心电图信号提供了强大而准确的解决方案.
这项研究强调了人工智能在彻底改变心脏诊断方面的巨大潜力.
取得的高精度表明,临床适用性可以改善患者的治疗结果.

关键词:

亚历克斯的网络亚历克斯的网络电动心电图信号电动心电图信号人工智能的人工智能是人工智能.优化的优化优化优化.业绩表现表现的表现表现是什么

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